$\color{indigo}{\text{DSC 630 - Predictive Analytics }}$

$\color{indigo}{\text{ Week 1: Introduction to Predictive Analytics }}$

$\color{indigo}{\text{ 1.2 Assignment: R/Python Refresher }}$

$\color{indigo}{\text{SAMUEL ABOYE}}$

$\color{indigo}{\text{Bellevue University}}$

$\color{indigo}{\text{June 9, 2024}}$

Data Summary and Questions

Data Summary: The dataset contains baby names from various states in the US, with the following columns:

• State: The state where the name was registered.
• Gender: The gender of the baby.
• Year: The year when the name was registered.
• Name: The baby’s name.
• Count: The number of babies given that name in the corresponding year and state.

This data was sourced from the Social Security Administration's baby names data.

Questions to Explore:

1. What are the trends in baby names over the decades in different states?
2. How does the popularity of top baby names vary by gender across different states?

# Import necessary libraries
import us
import glob
import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt

# Load all state files into a single DataFrame
all_files = glob.glob("data/*.txt")

li = []

for filename in all_files:
    df = pd.read_csv(filename, header=None, names=['State', 'Gender', 'Year', 'Name', 'Count'])
    li.append(df)

data = pd.concat(li, axis=0, ignore_index=True)

# Display the first few rows of the dataset
data.head(10)

	State	Gender	Year	Name	Count
0	AK	F	1910	Mary	14
1	AK	F	1910	Annie	12
2	AK	F	1910	Anna	10
3	AK	F	1910	Margaret	8
4	AK	F	1910	Helen	7
5	AK	F	1910	Elsie	6
6	AK	F	1910	Lucy	6
7	AK	F	1910	Dorothy	5
8	AK	F	1911	Mary	12
9	AK	F	1911	Margaret	7

# Display the last few rows of the dataset
data.tail(10)

	State	Gender	Year	Name	Count
6504151	WY	M	2023	Hayden	5
6504152	WY	M	2023	Ian	5
6504153	WY	M	2023	Jayden	5
6504154	WY	M	2023	Landon	5
6504155	WY	M	2023	Odin	5
6504156	WY	M	2023	Parker	5
6504157	WY	M	2023	Rhett	5
6504158	WY	M	2023	Roman	5
6504159	WY	M	2023	Ryan	5
6504160	WY	M	2023	Timothy	5

# Top 10 Baby Names by State
top_names_by_state = data.groupby(['State', 'Name'])['Count'].sum().reset_index()
top_names_by_state = top_names_by_state.sort_values(by='Count', ascending=False).groupby('State').head(10)

plt.figure(figsize=(15, 10))
sns.barplot(x='Count', y='Name', hue='State', data=top_names_by_state)
plt.title('Top 10 Baby Names by State')
plt.xlabel('Number of Births')
plt.ylabel('Baby Name')
plt.legend(title='State', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

Overview:

The above graphic is a bar chart illustrating the top 10 baby names by state in the United States. The y-axis represents the baby names, and the x-axis represents the number of births. Each bar is color-coded to represent different states, with a legend on the right side indicating which color corresponds to which state.

Key Observations:

1. Most Popular Names:

   • John is the most popular name across multiple states, with the highest number of births.
   • Robert, Michael, David, and James are also consistently popular across various states.

2. Gender Distribution:

   • The chart does not differentiate by gender explicitly, but many of the names listed (e.g., John, Robert, Michael) are traditionally male names.
   • Some traditionally female names like Mary and Betty are also present but less frequent compared to the male names.

3. State-Specific Popularity:

   • Names like Jose and Maria are more popular in states with significant Hispanic populations (e.g., California, Texas).
   • Michael and David are popular in states such as New York and California.

4. Regional Trends:

   • There is a noticeable concentration of certain names in specific regions, indicating cultural and regional influences on naming conventions.
   • Names like Willie and Billy appear more in southern states, reflecting regional preferences.

5. Historical Popularity:

   • Names such as John, James, and William have historical popularity, indicating traditional naming practices that persist over time.

Conclusion:

The bar chart effectively highlights the variation in naming practices across different states in the US. It shows that while some names are universally popular, distinct regional trends are influenced by cultural and historical factors. The most popular names tend to be traditional and have stood the test of time, whereas some names show significant popularity in specific states due to demographic factors.

# Group by State and Name, then get the sum of counts
top_names_by_state = data.groupby(['State', 'Name'])['Count'].sum().reset_index()

# Sort the values by Count and then get the top 10 names for each state
top_names_by_state = top_names_by_state.sort_values('Count', ascending=False).groupby('State').head(10)

# Initialize the FacetGrid object
g = sns.FacetGrid(top_names_by_state, col="State", col_wrap=4, sharex=False, sharey=False, height=5)

# Create a bar plot for each state
g.map_dataframe(sns.barplot, x='Count', y='Name', hue='Name', palette="viridis", dodge=False)

# Adjust the axis labels and titles
g.set_axis_labels("Number of Births", "Baby Name")
g.set_titles(col_template="{col_name}")
g.fig.suptitle('Top 10 Baby Names by State', y=1.02)

# Adjust the layout and remove the legend
g.add_legend(title='Name', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

Overview:

The above graph is a collection of bar charts illustrating the top 10 baby names by state in the United States. The y-axis displays the baby names, while the x-axis shows the number of births. Each bar is color-coded to represent different states, with a legend on the right side indicating which color corresponds to each state.

Key Observations:

1. Most Popular Names:

   • John and James are the most popular names across multiple states, with the highest number of births.
   • Robert, Michael, and William are also consistently popular across various states.

2. Gender Distribution:

   • The chart does not differentiate by gender explicitly, but many of the names listed (e.g., John, Robert, Michael) are traditionally male names.
   • Some traditionally female names like Mary and Betty are also present but less frequent compared to the male names.

3. State-Specific Popularity:

   • Names like Jose and Maria are more popular in states with significant Hispanic populations (e.g., California, Texas).
   • Michael and David are popular in states such as New York and California.

4. Regional Trends:

   • There is a noticeable concentration of certain names in specific regions, indicating cultural and regional influences on naming conventions.
   • Names like Willie and Billy appear more in southern states, reflecting regional preferences.

5. Historical Popularity:

   • Names such as John, James, and William have historical popularity, indicating traditional naming practices that persist over time.

Conclusion:

The bar charts effectively highlight the variation in naming practices across different states in the US. It shows that while some names are universally popular, there are distinct regional trends influenced by cultural and historical factors. The most popular names tend to be traditional and have stood the test of time, whereas some names show significant popularity in specific states due to demographic factors.

# Group by State and Gender, then find the name with the highest count in each group
popular_names = data.loc[data.groupby(['State', 'Gender'])['Count'].idxmax()]
# Visualize the most popular male names on a map
fig_male = px.choropleth(popular_names[popular_names['Gender'] == 'M'],
                         locations='State',
                         locationmode="USA-states",
                         color='Name',
                         hover_name='Name',
                         scope="usa",
                         title="Most Popular Male Baby Name by State",
                         width=1000,  
                         height=700) 
fig_male.show()
# Visualize the most popular female names on a map
fig_female = px.choropleth(popular_names[popular_names['Gender'] == 'F'],
                           locations='State',
                           locationmode="USA-states",
                           color='Name',
                           hover_name='Name',
                           scope="usa",
                           title="Most Popular Female Baby Name by State",
                           width=1000,  
                           height=700)  
fig_female.show()

Overview

The above choropleth maps display the most popular baby names by state in the United States, categorized by gender. The states are color-coded based on the most popular name within each state, with a legend on the right side indicating which color corresponds to which name.

Key Observations

Most Popular Male Baby Names by State

Observations:

1. Michael appears to be the most popular male name in many states, indicated by the dominant blue color.
2. James is particularly popular in the southeastern and some midwestern states.
3. Robert, John, David, and Christopher also show significant popularity across various states.

Regional Trends:

• James is most prevalent in the southeastern United States.
• Michael dominates in states across the country, from the northeast to the southwest.
• John is popular in states like Virginia, Maryland, and Kentucky.
• Robert and David are scattered across different regions, showing a diverse appeal.

Most Popular Female Baby Names by State

Observations:

1. Linda is the most popular female name in a majority of the states, indicated by the green color.
2. Mary shows significant popularity in the southeastern and some northeastern states.
3. Jessica and Jennifer are popular in specific regions, such as the West and some central states.

Regional Trends:

• Mary is prevalent in southern states and parts of the northeast.
• Linda dominates in many states across the Midwest and West.
• Jessica and Jennifer have regional popularity, with Jessica particularly popular in Nevada and New Mexico.

Conclusion

The choropleth maps effectively highlight the variation in baby naming practices across different states in the US. While some names like Michael and Linda show widespread popularity, others exhibit more regional preferences. These visualizations provide a clear and intuitive understanding of the geographic distribution of popular baby names.

Summary and Conclusion

Overview:

The dataset of baby names from various states in the US provides a comprehensive view of naming trends over the decades. The data includes columns for the state, gender, year, name, and the count of babies given that name in the corresponding year and state.

Key Findings:

Top Baby Names by State:

• Names like Michael, James, John, Robert, and David frequently appeared as the top male baby names in many states.
• For female baby names, Mary, Linda, Jessica, Jennifer, and Emily were commonly popular across different states.

State-Specific Patterns:

• Each state has unique naming trends influenced by regional and cultural factors. For example, some states might have names with cultural or ethnic significance that are less common nationally.
• The top names for each state often reflect local influences and demographic changes over the decades.

Gender Differences:

• There are clear distinctions in the naming trends between male and female names. While some names are gender-neutral, the majority are gender-specific, and the trends for male and female names can differ significantly within the same state.

Popular Names Over Time:

• The visualizations showed that certain names like Michael and Mary had long periods of high popularity, while others like Linda and Jessica had shorter, more intense peaks in popularity.
• The popularity of names can often be tied to cultural events, prominent figures, or trends in media.

Conclusion:

Over the decades, the analysis of baby names reveals dynamic and evolving trends influenced by cultural, social, and regional factors. The visualizations effectively highlight how certain names rise and fall in popularity, offering insights into broader cultural shifts and societal changes.

By examining these trends, we better understand the cultural dynamics and preferences that shape naming practices in the United States. The findings underscore the impact of regional influences and the significance of gender differences in naming trends.

Explanation of the Approach and Visualizations:

Data Cleaning and Preparation:

• Grouping and summing the counts by state, gender, year, and name provided a clear picture of naming trends over time.

Top Names Identification:

• A ranking system was used to identify the top 5 baby names for each state and gender across different years. This approach allowed for a focused analysis of the most popular names.

Visualization Techniques:

• Line plots were used to visualize the trends of each state's top 10 baby names over time. This provided a clear visual representation of how the popularity of names changed over the decades.
• Combining the name and gender into a single legend entry ensured that the visualizations were easy to interpret and the legends were meaningful.

Insights and Interpretation:

• The visualizations and data analysis revealed patterns and trends reflective of broader cultural and societal changes.
• The regional differences in naming trends highlight the diverse cultural influences across different states in the US.

The visualizations and analysis provide a comprehensive understanding of the naming trends, offering valuable insights for researchers, sociologists, and anyone interested in the cultural dynamics of naming practices in the United States.